MMR-and-RMR-workflow-Clutch58
Elliott Schmidt
2024-06-27
Load libraries (packages)
System1 - Dell
Importing data from firesting for resting
preexperiment_date <- "29 March 2023 10 51AM/All"
postexperiment_date <- "29 March 2023 05 13PM/All"
##--- last fish run in trial ---##
experiment_date <- "29 March 2023 01 22PM/Oxygen"
experiment_date2 <- "29 March 2023 01 22PM/All"
firesting <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1 <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_date2,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) System2 - Asus
Importing data from firesting for resting
preexperiment_date_asus <- "29 March 2023 11 01AM/All"
postexperiment_date_asus <- "29 March 2023 05 25PM/All"
##--- last fish run in trial ---##
experiment_date_asus <- "29 March 2023 02 01PM/Oxygen"
experiment_date2_asus <- "29 March 2023 02 01PM/All"
firesting_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19)
Cycle_1_asus <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE)
Cycle_last_asus <-read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_date2_asus,"slopes/Cycle_21.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) Chamber volumes
chamber1_dell = 0.04650
chamber2_dell = 0.04593
chamber3_dell = 0.04977
chamber4_dell = 0.04860
chamber1_asus = 0.04565
chamber2_asus = 0.04573+0.00385
chamber3_asus = 0.04551+0.00322
chamber4_asus = 0.04791+0.00277
Date_tested="2023-03-29"
Clutch = "58"
Male = "CVLA089"
Female = "CVLA059"
Population = "Vlassof cay"
Tank =370
salinity =36
Date_analysed = Sys.Date() Replicates
1
Enter specimen data
Replicate = 1
mass = 0.0006745
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_dell
system1 = "Dell"
Notes=" Third cycle used; resting may not be reliable"
##--- time of trail ---##
experiment_mmr_date <- "29 March 2023 01 12PM/Oxygen"
experiment_mmr_date2 <- "29 March 2023 01 12PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0007191883post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.002108974Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 7 8 10 11 12 13 15 16 19 21 22 23 24 26 27 28 29
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 7 1 334.3231 -0.03191953 0.999 NA 2906 3139 7453.82
## 2: 11 1 407.2980 -0.03238800 0.999 NA 4847 5078 9614.18
## 3: 16 1 482.0137 -0.03130068 0.997 NA 7292 7526 12313.81
## 4: 19 1 535.9387 -0.03152847 0.997 NA 8772 9006 13934.07
## 5: 20 1 526.4637 -0.02967275 0.994 NA 9266 9500 14473.44
## 6: 21 1 557.7471 -0.03066861 0.998 NA 9760 9993 15013.25
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7709.21 96.158 88.107 -0.03191953 -0.001260489 -0.03065904 -0.03065904
## 2: 9869.29 95.706 87.667 -0.03238800 -0.001560894 -0.03082711 -0.03082711
## 3: 12569.33 96.267 88.441 -0.03130068 -0.001936338 -0.02936435 -0.02936435
## 4: 14189.64 96.320 88.289 -0.03152847 -0.002161658 -0.02936681 -0.02936681
## 5: 14729.29 96.512 89.192 -0.02967275 -0.002236684 -0.02743607 -0.02743607
## 6: 15269.05 97.009 89.285 -0.03066861 -0.002311747 -0.02835686 -0.02835686
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0486 0.0006745 NA 36 30 1.013253 -0.3317674
## 2: %Air sec 0.0486 0.0006745 NA 36 30 1.013253 -0.3335860
## 3: %Air sec 0.0486 0.0006745 NA 36 30 1.013253 -0.3177572
## 4: %Air sec 0.0486 0.0006745 NA 36 30 1.013253 -0.3177839
## 5: %Air sec 0.0486 0.0006745 NA 36 30 1.013253 -0.2968910
## 6: %Air sec 0.0486 0.0006745 NA 36 30 1.013253 -0.3068550
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -491.8716 NA mgO2/hr/kg -491.8716
## 2: -494.5679 NA mgO2/hr/kg -494.5679
## 3: -471.1004 NA mgO2/hr/kg -471.1004
## 4: -471.1400 NA mgO2/hr/kg -471.1400
## 5: -440.1645 NA mgO2/hr/kg -440.1645
## 6: -454.9370 NA mgO2/hr/kg -454.9370
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 1 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006745 | ch4 | Dell | 0.0486 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 476.7234 | 0.3215499 | 0.998 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 8.08
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 6 7 8 10 11 12 13 14 15 16 18 19 20 22 23 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.37
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 274.9976 -0.04949774 0.9967350 NA 123 177 3663.10
## 2: NA 2 275.4288 -0.04947488 0.9981638 NA 214 268 3765.52
## 3: NA 3 274.9068 -0.04947405 0.9966776 NA 122 176 3662.02
## 4: NA 4 274.7055 -0.04941780 0.9965396 NA 124 178 3664.19
## 5: NA 5 275.0462 -0.04937433 0.9981835 NA 213 267 3764.16
## ---
## 210: NA 210 213.6078 -0.03276332 0.9967964 NA 69 123 3602.72
## 211: NA 211 213.4219 -0.03271251 0.9967697 NA 71 125 3604.97
## 212: NA 212 213.3377 -0.03268907 0.9968103 NA 70 124 3603.89
## 213: NA 213 213.0814 -0.03257365 0.9957398 NA 2 56 3527.22
## 214: NA 214 212.3295 -0.03236193 0.9961030 NA 1 55 3526.06
## endtime oxy endoxy rate
## 1: 3723.10 93.603 90.795 -0.04949774
## 2: 3825.52 89.136 86.163 -0.04947488
## 3: 3722.02 93.638 90.815 -0.04947405
## 4: 3724.19 93.581 90.768 -0.04941780
## 5: 3824.16 89.198 86.212 -0.04937433
## ---
## 210: 3662.72 95.603 93.603 -0.03276332
## 211: 3664.97 95.546 93.498 -0.03271251
## 212: 3663.89 95.589 93.581 -0.03268907
## 213: 3587.22 98.174 96.147 -0.03257365
## 214: 3586.06 98.194 96.217 -0.03236193
##
## Regressions : 214 | Results : 214 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 214 adjusted rate(s):
## Rate : -0.04949774
## Adjustment : -0.0007191883
## Adjusted Rate : -0.04877855
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 214 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 213 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time endtime
## 1: NA 1 274.9976 -0.04949774 0.996735 NA 123 177 3663.1 3723.1
## oxy endoxy rate adjustment rate.adjusted rate.input oxy.unit
## 1: 93.603 90.795 -0.04949774 -0.0007191883 -0.04877855 -0.04877855 %Air
## time.unit volume mass area S t P rate.abs rate.m.spec
## 1: sec 0.0486 0.0006745 NA 36 30 1.013253 -0.5278421 -782.568
## rate.a.spec output.unit rate.output
## 1: NA mgO2/hr/kg -782.568
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 1 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006745 | ch4 | Dell | 0.0486 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 476.7234 | 0.3215499 | 0.998 | 782.568 | 0.5278421 | 0.996735 | 305.8446 | 0.2062922 | Third cycle used; resting may not be reliable |
Exporting data
## Rows: 278 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.2
Enter specimen data
Replicate = 2
mass = 0.0006702
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "29 March 2023 12 36PM/Oxygen"
experiment_mmr_date2 <- "29 March 2023 12 36PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.00054071post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 4.367777e-14Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 7 8 10 11 12 13 15 16 19 21 22 23 24 26 27 28 29
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 9 1 203.3669 -0.012520846 0.986 NA 3891 4123 8533.77
## 2: 11 1 205.6548 -0.011335979 0.995 NA 4847 5078 9614.18
## 3: 12 1 186.5556 -0.008949916 0.982 NA 5330 5563 10153.25
## 4: 14 1 235.3775 -0.012327997 0.996 NA 6310 6543 11234.49
## 5: 16 1 226.1183 -0.010538734 0.984 NA 7292 7526 12313.81
## 6: 17 1 208.5758 -0.008730505 0.969 NA 7787 8021 12854.32
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 8788.93 96.306 93.140 -0.012520846 -2.716887e-04 -0.012249157 -0.012249157
## 2: 9869.29 96.625 93.686 -0.011335979 -2.132361e-04 -0.011122742 -0.011122742
## 3: 10409.20 96.097 93.389 -0.008949916 -1.840478e-04 -0.008765868 -0.008765868
## 4: 11489.18 96.791 93.551 -0.012327997 -1.255831e-04 -0.012202414 -0.012202414
## 5: 12569.33 96.310 93.643 -0.010538734 -6.716566e-05 -0.010471569 -0.010471569
## 6: 13109.81 96.654 93.951 -0.008730505 -3.792301e-05 -0.008692582 -0.008692582
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04977 0.0006702 NA 36 30 1.013253 -0.13574152
## 2: %Air sec 0.04977 0.0006702 NA 36 30 1.013253 -0.12325892
## 3: %Air sec 0.04977 0.0006702 NA 36 30 1.013253 -0.09714074
## 4: %Air sec 0.04977 0.0006702 NA 36 30 1.013253 -0.13522352
## 5: %Air sec 0.04977 0.0006702 NA 36 30 1.013253 -0.11604281
## 6: %Air sec 0.04977 0.0006702 NA 36 30 1.013253 -0.09632860
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -202.5388 NA mgO2/hr/kg -202.5388
## 2: -183.9136 NA mgO2/hr/kg -183.9136
## 3: -144.9429 NA mgO2/hr/kg -144.9429
## 4: -201.7659 NA mgO2/hr/kg -201.7659
## 5: -173.1465 NA mgO2/hr/kg -173.1465
## 6: -143.7311 NA mgO2/hr/kg -143.7311
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 2 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006702 | ch3 | Dell | 0.04977 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 181.2616 | 0.1214815 | 0.9886 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 8.08
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 4 5 7 8 9 12 13 14 15 16 18 19 20 21 22 23 26 27
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.74
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 176.0138 -0.05397230 0.9922047 NA 155 209 1558.80
## 2: NA 2 175.9611 -0.05393989 0.9921680 NA 154 208 1557.70
## 3: NA 3 175.8069 -0.05384097 0.9920349 NA 156 210 1560.20
## 4: NA 4 175.6885 -0.05376875 0.9920197 NA 153 207 1556.61
## 5: NA 5 175.5924 -0.05370434 0.9917646 NA 157 211 1561.38
## ---
## 209: NA 209 133.8097 -0.02494902 0.9291880 NA 5 59 1390.39
## 210: NA 210 132.8110 -0.02424292 0.9326537 NA 4 58 1389.30
## 211: NA 211 131.9096 -0.02360473 0.9368168 NA 3 57 1388.21
## 212: NA 212 131.0514 -0.02299608 0.9421698 NA 2 56 1387.12
## 213: NA 213 130.3856 -0.02252331 0.9458190 NA 1 55 1386.02
## endtime oxy endoxy rate
## 1: 1618.80 91.842 88.667 -0.05397230
## 2: 1617.70 91.871 88.683 -0.05393989
## 3: 1620.20 91.811 88.655 -0.05384097
## 4: 1616.61 91.868 88.747 -0.05376875
## 5: 1621.38 91.721 88.630 -0.05370434
## ---
## 209: 1450.39 99.040 97.338 -0.02494902
## 210: 1449.30 99.077 97.399 -0.02424292
## 211: 1448.21 99.109 97.444 -0.02360473
## 212: 1447.12 99.141 97.545 -0.02299608
## 213: 1446.02 99.129 97.619 -0.02252331
##
## Regressions : 213 | Results : 213 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 213 adjusted rate(s):
## Rate : -0.0539723
## Adjustment : -0.00054071
## Adjusted Rate : -0.05343159
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 5 rate(s) removed, 208 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 207 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time endtime
## 1: NA 1 176.0138 -0.0539723 0.9922047 NA 155 209 1558.8 1618.8
## oxy endoxy rate adjustment rate.adjusted rate.input oxy.unit
## 1: 91.842 88.667 -0.0539723 -0.00054071 -0.05343159 -0.05343159 %Air
## time.unit volume mass area S t P rate.abs rate.m.spec
## 1: sec 0.04977 0.0006702 NA 36 30 1.013253 -0.5921129 -883.4869
## rate.a.spec output.unit rate.output
## 1: NA mgO2/hr/kg -883.4869
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 2 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006702 | ch3 | Dell | 0.04977 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 181.2616 | 0.1214815 | 0.9886 | 883.4869 | 0.5921129 | 0.9922047 | 702.2254 | 0.4706314 |
Exporting data
## Rows: 279 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.3
Enter specimen data
Replicate = 3
mass = 0.0006270
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "29 March 2023 01 12PM/Oxygen"
experiment_mmr_date2 <- "29 March 2023 01 12PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.000527518post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.003258395Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 7 8 10 11 12 13 15 16 19 21 22 23 24 26 27 28 29
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 9 1 328.7194 -0.02740413 0.997 NA 3891 4123 8533.77
## 2: 16 1 442.3729 -0.02819383 0.988 NA 7292 7526 12313.81
## 3: 17 1 460.7593 -0.02845608 0.997 NA 7787 8021 12854.32
## 4: 18 1 428.5623 -0.02490987 0.995 NA 8281 8511 13393.84
## 5: 19 1 458.0723 -0.02606317 0.994 NA 8772 9006 13934.07
## 6: 21 1 523.4723 -0.02853051 0.997 NA 9760 9993 15013.25
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 8788.93 94.687 87.876 -0.02740413 -0.001886221 -0.02551790 -0.02551790
## 2: 12569.33 94.839 87.520 -0.02819383 -0.002919172 -0.02527466 -0.02527466
## 3: 13109.81 94.903 87.851 -0.02845608 -0.003066863 -0.02538922 -0.02538922
## 4: 13648.81 95.018 88.607 -0.02490987 -0.003214217 -0.02169565 -0.02169565
## 5: 14189.64 94.492 87.944 -0.02606317 -0.003361918 -0.02270125 -0.02270125
## 6: 15269.05 94.900 87.604 -0.02853051 -0.003656837 -0.02487367 -0.02487367
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.000627 NA 36 30 1.013253 -0.2609638
## 2: %Air sec 0.04593 0.000627 NA 36 30 1.013253 -0.2584762
## 3: %Air sec 0.04593 0.000627 NA 36 30 1.013253 -0.2596478
## 4: %Air sec 0.04593 0.000627 NA 36 30 1.013253 -0.2218748
## 5: %Air sec 0.04593 0.000627 NA 36 30 1.013253 -0.2321588
## 6: %Air sec 0.04593 0.000627 NA 36 30 1.013253 -0.2543754
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -416.2102 NA mgO2/hr/kg -416.2102
## 2: -412.2428 NA mgO2/hr/kg -412.2428
## 3: -414.1113 NA mgO2/hr/kg -414.1113
## 4: -353.8673 NA mgO2/hr/kg -353.8673
## 5: -370.2692 NA mgO2/hr/kg -370.2692
## 6: -405.7024 NA mgO2/hr/kg -405.7024
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 3 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.000627 | ch2 | Dell | 0.04593 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 403.7072 | 0.2531244 | 0.9946 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 8.08
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 6 7 8 10 11 12 13 14 15 16 18 19 20 22 23 25
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.37
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 299.1318 -0.05671725 0.9929385 NA 214 268 3765.52
## 2: NA 2 298.9669 -0.05667288 0.9934346 NA 213 267 3764.16
## 3: NA 3 298.5385 -0.05655958 0.9934802 NA 212 266 3763.07
## 4: NA 4 298.4167 -0.05652730 0.9934731 NA 211 265 3761.98
## 5: NA 5 298.1160 -0.05644820 0.9934106 NA 210 264 3760.86
## ---
## 210: NA 210 196.5497 -0.02866376 0.9812154 NA 5 59 3530.59
## 211: NA 211 195.5573 -0.02838088 0.9833179 NA 1 55 3526.06
## 212: NA 212 195.3344 -0.02832141 0.9833386 NA 4 58 3529.39
## 213: NA 213 194.9736 -0.02821868 0.9843174 NA 3 57 3528.31
## 214: NA 214 194.9014 -0.02819741 0.9844543 NA 2 56 3527.22
## endtime oxy endoxy rate
## 1: 3825.52 85.683 82.054 -0.05671725
## 2: 3824.16 85.699 82.177 -0.05667288
## 3: 3823.07 85.763 82.282 -0.05655958
## 4: 3821.98 85.776 82.370 -0.05652730
## 5: 3820.86 85.746 82.456 -0.05644820
## ---
## 210: 3590.59 95.362 93.449 -0.02866376
## 211: 3586.06 95.713 93.701 -0.02838088
## 212: 3589.39 95.495 93.522 -0.02832141
## 213: 3588.31 95.512 93.619 -0.02821868
## 214: 3587.22 95.602 93.709 -0.02819741
##
## Regressions : 214 | Results : 214 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 214 adjusted rate(s):
## Rate : -0.05671725
## Adjustment : -0.000527518
## Adjusted Rate : -0.05618973
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 214 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 213 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 299.1318 -0.05671725 0.9929385 NA 214 268 3765.52
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 3825.52 85.683 82.054 -0.05671725 -0.000527518 -0.05618973 -0.05618973
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04593 0.000627 NA 36 30 1.013253 -0.5746353
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -916.4837 NA mgO2/hr/kg -916.4837
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 3 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.000627 | ch2 | Dell | 0.04593 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 403.7072 | 0.2531244 | 0.9946 | 916.4837 | 0.5746353 | 0.9929385 | 512.7765 | 0.3215108 |
Exporting data
## Rows: 280 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.4
Enter specimen data
Replicate = 4
mass = 0.0006959
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_dell
system1 = "Dell"
Notes=""
##--- time of trail ---##
experiment_mmr_date <- "29 March 2023 01 22PM/Oxygen"
experiment_mmr_date2 <- "29 March 2023 01 22PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",experiment_mmr_date2,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",preexperiment_date,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] 0.0002187212post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Dell/Experiment_",postexperiment_date,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.004020924Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2$TIME ==Cycle_1$Time[1], firesting$TIME)
Tstart.dTIME=as.numeric(firesting2[Tstart.row, "dTIME"])
Tend.row=which(firesting2$TIME ==tail(Cycle_last$Time, n=1), firesting$TIME)
Tend.dTIME=as.numeric(firesting2[Tend.row, "dTIME"])
apoly_insp <- firesting2 |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 7 8 10 11 12 13 15 16 19 21 22 23 24 26 27 28 29
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from selected replicates...
## To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## Warning: adjust_rate: background rates in 'by' and 'by2' differ in sign (i.e. one is +ve, one is -ve).
## Ensure this is correct. The 'linear' adjustment has been performed regardless.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 1 rate(s) removed, 20 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 14 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 16 1 441.7243 -0.02794850 0.998 NA 7292 7526 12313.81
## 2: 17 1 436.6815 -0.02639007 0.995 NA 7787 8021 12854.32
## 3: 18 1 462.0458 -0.02720637 0.992 NA 8281 8511 13393.84
## 4: 19 1 492.1329 -0.02828590 0.994 NA 8772 9006 13934.07
## 5: 20 1 475.2077 -0.02604222 0.996 NA 9266 9500 14473.44
## 6: 21 1 520.7098 -0.02811973 0.989 NA 9760 9993 15013.25
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 12569.33 97.650 90.377 -0.02794850 -0.003494286 -0.02445422 -0.02445422
## 2: 13109.81 97.451 90.713 -0.02639007 -0.003723574 -0.02266649 -0.02266649
## 3: 13648.81 97.385 90.809 -0.02720637 -0.003952338 -0.02325403 -0.02325403
## 4: 14189.64 97.783 90.866 -0.02828590 -0.004181641 -0.02410426 -0.02410426
## 5: 14729.29 98.494 91.825 -0.02604222 -0.004410512 -0.02163171 -0.02163171
## 6: 15269.05 98.330 90.992 -0.02811973 -0.004639499 -0.02348023 -0.02348023
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0006959 NA 36 30 1.013253 -0.2531894
## 2: %Air sec 0.0465 0.0006959 NA 36 30 1.013253 -0.2346800
## 3: %Air sec 0.0465 0.0006959 NA 36 30 1.013253 -0.2407632
## 4: %Air sec 0.0465 0.0006959 NA 36 30 1.013253 -0.2495661
## 5: %Air sec 0.0465 0.0006959 NA 36 30 1.013253 -0.2239663
## 6: %Air sec 0.0465 0.0006959 NA 36 30 1.013253 -0.2431051
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -363.8302 NA mgO2/hr/kg -363.8302
## 2: -337.2324 NA mgO2/hr/kg -337.2324
## 3: -345.9738 NA mgO2/hr/kg -345.9738
## 4: -358.6235 NA mgO2/hr/kg -358.6235
## 5: -321.8368 NA mgO2/hr/kg -321.8368
## 6: -349.3392 NA mgO2/hr/kg -349.3392
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 4 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006959 | ch1 | Dell | 0.0465 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 350.9998 | 0.2442608 | 0.9936 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.07 11.47
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 3 4 7 8 10 11 12 13 15 16 19 21 22 23 24 26 27 28 29
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.08 1.37
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 416.7597 -0.07647676 0.9983322 NA 14 67 4183.83
## 2: NA 2 416.6051 -0.07643915 0.9982814 NA 15 68 4184.92
## 3: NA 3 416.2323 -0.07635303 0.9982260 NA 12 65 4181.54
## 4: NA 4 415.9840 -0.07629475 0.9981785 NA 11 64 4180.45
## 5: NA 5 415.9259 -0.07628012 0.9983016 NA 13 66 4182.63
## ---
## 207: NA 207 285.8386 -0.04603608 0.9936678 NA 165 218 4356.33
## 208: NA 208 285.5867 -0.04597818 0.9937499 NA 164 217 4355.12
## 209: NA 209 285.2433 -0.04589979 0.9937515 NA 163 216 4354.03
## 210: NA 210 285.1598 -0.04588158 0.9937374 NA 161 214 4351.73
## 211: NA 211 285.0278 -0.04585102 0.9936753 NA 162 215 4352.81
## endtime oxy endoxy rate
## 1: 4243.83 96.682 92.293 -0.07647676
## 2: 4244.92 96.621 92.239 -0.07643915
## 3: 4241.54 96.883 92.438 -0.07635303
## 4: 4240.45 96.949 92.535 -0.07629475
## 5: 4242.63 96.792 92.336 -0.07628012
## ---
## 207: 4416.33 85.365 82.460 -0.04603608
## 208: 4415.12 85.364 82.559 -0.04597818
## 209: 4414.03 85.377 82.673 -0.04589979
## 210: 4411.73 85.551 82.813 -0.04588158
## 211: 4412.81 85.442 82.753 -0.04585102
##
## Regressions : 211 | Results : 211 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 211 adjusted rate(s):
## Rate : -0.07647676
## Adjustment : 0.0002187212
## Adjusted Rate : -0.07669548
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 211 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 210 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 416.7597 -0.07647676 0.9983322 NA 14 67 4183.83
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 4243.83 96.682 92.293 -0.07647676 0.0002187212 -0.07669548 -0.07669548
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.0465 0.0006959 NA 36 30 1.013253 -0.7940751
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1141.076 NA mgO2/hr/kg -1141.076
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 4 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006959 | ch1 | Dell | 0.0465 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 350.9998 | 0.2442608 | 0.9936 | 1141.076 | 0.7940751 | 0.9983322 | 790.0766 | 0.5498143 |
Exporting data
## Rows: 281 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.5
Enter specimen data
Replicate = 5
mass = 0.0006945
chamber = "ch4"
Swim = "good/good"
chamber_vol = chamber4_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "29 March 2023 01 06PM/Oxygen"
experiment_mmr_date2_asus <- "29 March 2023 01 06PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0009354826post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.002205028Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.45
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=255,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2_asus$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 2 rate(s) removed, 19 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 13 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 2 1 392.0879 -0.02584251 0.992 NA 429 617 11457.16
## 2: 4 1 416.4030 -0.02552261 0.986 NA 1223 1411 12536.01
## 3: 5 1 439.2418 -0.02622544 0.996 NA 1622 1810 13077.15
## 4: 6 1 394.9362 -0.02196266 0.981 NA 2019 2207 13616.48
## 5: 9 1 411.2506 -0.02064474 0.968 NA 3212 3400 15236.39
## 6: 10 1 486.3127 -0.02472038 0.985 NA 3610 3798 15776.28
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 11712.91 95.516 89.291 -0.02584251 -0.001497520 -0.02434499 -0.02434499
## 2: 12791.05 96.004 89.577 -0.02552261 -0.001665149 -0.02385746 -0.02385746
## 3: 13332.61 96.092 89.672 -0.02622544 -0.001749291 -0.02447615 -0.02447615
## 4: 13871.49 95.617 89.852 -0.02196266 -0.001833084 -0.02012958 -0.02012958
## 5: 15491.23 96.139 90.939 -0.02064474 -0.002084852 -0.01855989 -0.01855989
## 6: 16031.73 95.817 89.524 -0.02472038 -0.002168814 -0.02255156 -0.02255156
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.05068 0.0006945 NA 36 30 1.013253 -0.2747167
## 2: %Air sec 0.05068 0.0006945 NA 36 30 1.013253 -0.2692152
## 3: %Air sec 0.05068 0.0006945 NA 36 30 1.013253 -0.2761967
## 4: %Air sec 0.05068 0.0006945 NA 36 30 1.013253 -0.2271486
## 5: %Air sec 0.05068 0.0006945 NA 36 30 1.013253 -0.2094358
## 6: %Air sec 0.05068 0.0006945 NA 36 30 1.013253 -0.2544791
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -395.5604 NA mgO2/hr/kg -395.5604
## 2: -387.6389 NA mgO2/hr/kg -387.6389
## 3: -397.6915 NA mgO2/hr/kg -397.6915
## 4: -327.0679 NA mgO2/hr/kg -327.0679
## 5: -301.5634 NA mgO2/hr/kg -301.5634
## 6: -366.4206 NA mgO2/hr/kg -366.4206
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 5 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006945 | ch4 | Asus | 0.05068 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 374.8758 | 0.2603513 | 0.988 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch4
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 8 9 10 11 12 13 15 16 18 19 20 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.66
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 585.5126 -0.06372426 0.9859683 NA 88 132 7690.90
## 2: NA 2 585.3134 -0.06369743 0.9859003 NA 89 133 7692.26
## 3: NA 3 585.2927 -0.06369381 0.9859034 NA 90 134 7693.61
## 4: NA 4 584.1420 -0.06354770 0.9854085 NA 87 131 7689.54
## 5: NA 5 582.6630 -0.06335276 0.9851803 NA 91 135 7694.97
## ---
## 173: NA 173 252.7966 -0.02043200 0.9746128 NA 5 49 7577.63
## 174: NA 174 252.5090 -0.02039461 0.9741305 NA 4 48 7576.27
## 175: NA 175 250.9060 -0.02018414 0.9721313 NA 3 47 7574.91
## 176: NA 176 247.8692 -0.01978516 0.9674780 NA 2 46 7573.55
## 177: NA 177 243.7610 -0.01924508 0.9623402 NA 1 45 7572.21
## endtime oxy endoxy rate
## 1: 7750.90 95.214 91.716 -0.06372426
## 2: 7752.26 95.169 91.692 -0.06369743
## 3: 7753.61 95.104 91.591 -0.06369381
## 4: 7749.54 95.236 91.812 -0.06354770
## 5: 7754.97 95.145 91.538 -0.06335276
## ---
## 173: 7637.63 97.890 96.806 -0.02043200
## 174: 7636.27 97.898 96.771 -0.02039461
## 175: 7634.91 97.862 96.774 -0.02018414
## 176: 7633.55 97.860 96.763 -0.01978516
## 177: 7632.21 97.873 96.805 -0.01924508
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.06372426
## Adjustment : -0.0009354826
## Adjusted Rate : -0.06278878
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 6 rate(s) removed, 171 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 170 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 585.5126 -0.06372426 0.9859683 NA 88 132 7690.9
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 7750.9 95.214 91.716 -0.06372426 -0.0009354826 -0.06278878 -0.06278878
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.05068 0.0006945 NA 36 30 1.013253 -0.7085288
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1020.2 NA mgO2/hr/kg -1020.2
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 5 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0006945 | ch4 | Asus | 0.05068 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 374.8758 | 0.2603513 | 0.988 | 1020.2 | 0.7085288 | 0.9859683 | 645.324 | 0.4481775 | ||
| ### Expor | ting data |
## Rows: 282 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.6
Enter specimen data
Replicate = 6
mass = 0.0005840
chamber = "ch3"
Swim = "good/good"
chamber_vol = chamber3_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "29 March 2023 01 40PM/Oxygen"
experiment_mmr_date2_asus <- "29 March 2023 01 40PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.002325513post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.004567091Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.45
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 8 1 424.1207 -0.02253417 0.970 NA 2815 2995 14697.04
## 2: 9 1 474.8092 -0.02497142 0.952 NA 3212 3393 15236.39
## 3: 10 1 512.2903 -0.02650303 0.994 NA 3610 3790 15776.28
## 4: 12 1 558.8756 -0.02753288 0.991 NA 4405 4585 16856.21
## 5: 17 1 623.7411 -0.02711113 0.993 NA 6393 6573 19557.28
## 6: 20 1 725.8982 -0.02983727 0.991 NA 7584 7764 21176.39
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 14941.98 93.070 86.751 -0.02253417 -0.004205527 -0.01832865 -0.01832865
## 2: 15481.72 94.806 88.468 -0.02497142 -0.004353597 -0.02061782 -0.02061782
## 3: 16020.92 94.181 87.557 -0.02650303 -0.004501666 -0.02200137 -0.02200137
## 4: 17101.05 94.765 88.503 -0.02753288 -0.004798063 -0.02273482 -0.02273482
## 5: 19801.84 93.774 87.135 -0.02711113 -0.005539291 -0.02157184 -0.02157184
## 6: 21421.32 93.882 86.431 -0.02983727 -0.005983680 -0.02385359 -0.02385359
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04873 0.000584 NA 36 30 1.013253 -0.1988683
## 2: %Air sec 0.04873 0.000584 NA 36 30 1.013253 -0.2237062
## 3: %Air sec 0.04873 0.000584 NA 36 30 1.013253 -0.2387178
## 4: %Air sec 0.04873 0.000584 NA 36 30 1.013253 -0.2466759
## 5: %Air sec 0.04873 0.000584 NA 36 30 1.013253 -0.2340574
## 6: %Air sec 0.04873 0.000584 NA 36 30 1.013253 -0.2588147
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -340.5280 NA mgO2/hr/kg -340.5280
## 2: -383.0585 NA mgO2/hr/kg -383.0585
## 3: -408.7634 NA mgO2/hr/kg -408.7634
## 4: -422.3902 NA mgO2/hr/kg -422.3902
## 5: -400.7833 NA mgO2/hr/kg -400.7833
## 6: -443.1758 NA mgO2/hr/kg -443.1758
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 6 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.000584 | ch3 | Asus | 0.04873 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 411.6343 | 0.2403944 | 0.9842 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch3
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 5 6 7 8 9 11 12 13 14 16 17 18 21 22 23 24 26
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.49
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 642.1170 -0.05666199 0.9867025 NA 34 79 9669.38
## 2: NA 2 641.3477 -0.05658429 0.9863524 NA 32 77 9666.66
## 3: NA 3 641.3152 -0.05658023 0.9863387 NA 33 78 9668.02
## 4: NA 4 641.2330 -0.05657293 0.9863239 NA 31 76 9665.27
## 5: NA 5 641.1476 -0.05656471 0.9863043 NA 30 75 9663.93
## ---
## 173: NA 173 402.2387 -0.03206212 0.9813675 NA 123 168 9790.25
## 174: NA 174 400.5236 -0.03188921 0.9816397 NA 127 172 9795.79
## 175: NA 175 399.9147 -0.03182585 0.9822653 NA 124 169 9791.62
## 176: NA 176 399.4169 -0.03177603 0.9823446 NA 126 171 9794.30
## 177: NA 177 398.3965 -0.03167168 0.9829406 NA 125 170 9792.96
## endtime oxy endoxy rate
## 1: 9729.38 94.067 91.009 -0.05666199
## 2: 9726.66 94.317 91.072 -0.05658429
## 3: 9728.02 94.201 91.052 -0.05658023
## 4: 9725.27 94.367 91.170 -0.05657293
## 5: 9723.93 94.386 91.197 -0.05656471
## ---
## 173: 9850.25 88.505 86.464 -0.03206212
## 174: 9855.79 88.274 86.090 -0.03188921
## 175: 9851.62 88.460 86.351 -0.03182585
## 176: 9854.30 88.308 86.160 -0.03177603
## 177: 9852.96 88.396 86.284 -0.03167168
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.05666199
## Adjustment : -0.002325513
## Adjusted Rate : -0.05433647
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 9 rate(s) removed, 168 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 167 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 642.117 -0.05666199 0.9867025 NA 34 79 9669.38
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 9729.38 94.067 91.009 -0.05666199 -0.002325513 -0.05433647 -0.05433647
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04873 0.000584 NA 36 30 1.013253 -0.5895582
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1009.517 NA mgO2/hr/kg -1009.517
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 6 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.000584 | ch3 | Asus | 0.04873 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 411.6343 | 0.2403944 | 0.9842 | 1009.517 | 0.5895582 | 0.9867025 | 597.8832 | 0.3491638 | ||
| ### Expor | ting data |
## Rows: 283 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.7
Enter specimen data
Replicate = 7
mass = 0.0003390
chamber = "ch2"
Swim = "good/good"
chamber_vol = chamber2_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "29 March 2023 01 50PM/Oxygen"
experiment_mmr_date2_asus <- "29 March 2023 01 50PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.0004873032post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.00447246Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.45
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 10 1 278.3798 -0.01150243 0.979 NA 3610 3790 15776.28
## 2: 15 1 296.6721 -0.01078965 0.996 NA 5597 5777 18476.88
## 3: 17 1 322.0019 -0.01148399 0.992 NA 6393 6573 19557.28
## 4: 19 1 403.2040 -0.01479008 0.977 NA 7188 7368 20637.50
## 5: 20 1 387.1707 -0.01365143 0.950 NA 7584 7764 21176.39
## 6: 21 1 410.9996 -0.01440969 0.990 NA 7981 8161 21715.91
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 16020.92 96.868 94.015 -0.01150243 -0.004356145 -0.007146288 -0.007146288
## 2: 18721.61 97.300 94.614 -0.01078965 -0.005673786 -0.005115863 -0.005115863
## 3: 19801.84 97.480 94.669 -0.01148399 -0.006200870 -0.005283123 -0.005283123
## 4: 20882.92 97.864 94.254 -0.01479008 -0.006728118 -0.008061958 -0.008061958
## 5: 21421.32 97.751 94.053 -0.01365143 -0.006990922 -0.006660507 -0.006660507
## 6: 21960.94 97.956 94.501 -0.01440969 -0.007254178 -0.007155508 -0.007155508
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04958 0.000339 NA 36 30 1.013253 -0.07889071
## 2: %Air sec 0.04958 0.000339 NA 36 30 1.013253 -0.05647604
## 3: %Air sec 0.04958 0.000339 NA 36 30 1.013253 -0.05832249
## 4: %Air sec 0.04958 0.000339 NA 36 30 1.013253 -0.08899915
## 5: %Air sec 0.04958 0.000339 NA 36 30 1.013253 -0.07352798
## 6: %Air sec 0.04958 0.000339 NA 36 30 1.013253 -0.07899249
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -232.7160 NA mgO2/hr/kg -232.7160
## 2: -166.5960 NA mgO2/hr/kg -166.5960
## 3: -172.0427 NA mgO2/hr/kg -172.0427
## 4: -262.5344 NA mgO2/hr/kg -262.5344
## 5: -216.8967 NA mgO2/hr/kg -216.8967
## 6: -233.0162 NA mgO2/hr/kg -233.0162
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 7 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.000339 | ch2 | Asus | 0.04958 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 223.4412 | 0.0757466 | 0.9776 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch2
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 1.61
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 433.0206 -0.03286188 0.9976055 NA 21 66 10269.29
## 2: NA 2 432.8726 -0.03284729 0.9975552 NA 22 67 10270.62
## 3: NA 3 432.7595 -0.03283612 0.9975203 NA 23 68 10271.99
## 4: NA 4 432.5316 -0.03281458 0.9974794 NA 20 65 10267.95
## 5: NA 5 431.6978 -0.03273285 0.9971756 NA 24 69 10273.35
## ---
## 173: NA 173 334.7528 -0.02341679 0.9899109 NA 173 218 10475.86
## 174: NA 174 330.5126 -0.02301299 0.9869031 NA 174 219 10477.22
## 175: NA 175 326.0791 -0.02259087 0.9837165 NA 175 220 10478.57
## 176: NA 176 322.0432 -0.02220676 0.9822506 NA 176 221 10479.91
## 177: NA 177 320.0423 -0.02201635 0.9817191 NA 177 222 10481.25
## endtime oxy endoxy rate
## 1: 10329.29 95.537 93.612 -0.03286188
## 2: 10330.62 95.501 93.593 -0.03284729
## 3: 10331.99 95.463 93.544 -0.03283612
## 4: 10327.95 95.558 93.642 -0.03281458
## 5: 10333.35 95.433 93.531 -0.03273285
## ---
## 173: 10535.86 89.488 88.166 -0.02341679
## 174: 10537.22 89.465 88.159 -0.02301299
## 175: 10538.57 89.431 88.144 -0.02259087
## 176: 10539.91 89.414 88.086 -0.02220676
## 177: 10541.25 89.341 88.012 -0.02201635
##
## Regressions : 177 | Results : 177 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 177 adjusted rate(s):
## Rate : -0.03286188
## Adjustment : -0.0004873032
## Adjusted Rate : -0.03237458
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 177 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 176 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 433.0206 -0.03286188 0.9976055 NA 21 66 10269.29
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 10329.29 95.537 93.612 -0.03286188 -0.0004873032 -0.03237458 -0.03237458
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04958 0.000339 NA 36 30 1.013253 -0.3573959
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -1054.265 NA mgO2/hr/kg -1054.265
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 7 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.000339 | ch2 | Asus | 0.04958 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 223.4412 | 0.0757466 | 0.9776 | 1054.265 | 0.3573959 | 0.9976055 | 830.8239 | 0.2816493 | ||
| ### Expor | ting data |
## Rows: 284 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.8
Enter specimen data
Replicate = 8
mass = 0.0005429
chamber = "ch1"
Swim = "good/good"
chamber_vol = chamber1_asus
system1 = "Asus"
Notes=""
##--- time of trail ---##
experiment_mmr_date_asus <- "29 March 2023 02 01PM/Oxygen"
experiment_mmr_date2_asus <- "29 March 2023 02 01PM/All"
firesting_mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date_asus,"data raw/Firesting.txt"),
delim = "\t", escape_double = FALSE,
col_types = cols(`Time (HH:MM:SS)` = col_time(format = "%H:%M:%S"),
`Time (s)` = col_number(), Ch1...5 = col_number(),
Ch2...6 = col_number(), Ch3...7 = col_number(),
Ch4...8 = col_number()), trim_ws = TRUE,
skip = 19) ## New names:
## • `Ch1` -> `Ch1...5`
## • `Ch2` -> `Ch2...6`
## • `Ch3` -> `Ch3...7`
## • `Ch4` -> `Ch4...8`
## • `Ch 1` -> `Ch 1...9`
## • `Ch 2` -> `Ch 2...10`
## • `Ch 3` -> `Ch 3...11`
## • `Ch 4` -> `Ch 4...12`
## • `('C)` -> `('C)...15`
## • `('C)` -> `('C)...16`
## • `Ch 1` -> `Ch 1...18`
## • `Ch 2` -> `Ch 2...19`
## • `Ch 3` -> `Ch 3...20`
## • `Ch 4` -> `Ch 4...21`
## • `Ch1` -> `Ch1...22`
## • `Ch2` -> `Ch2...23`
## • `Ch3` -> `Ch3...24`
## • `Ch4` -> `Ch4...25`
## • `Ch1` -> `Ch1...26`
## • `Ch2` -> `Ch2...27`
## • `Ch3` -> `Ch3...28`
## • `Ch4` -> `Ch4...29`
## • `` -> `...31`## Warning: One or more parsing issues, call `problems()` on your data frame for details,
## e.g.:
## dat <- vroom(...)
## problems(dat)Cycle_1.mmr <- read_delim(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",experiment_mmr_date2_asus,"slopes/Cycle_1.txt"),
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
`Seconds from start for linreg` = col_number(),
`ch1 po2` = col_number(), `ch2 po2` = col_number(),
`ch3 po2` = col_number(), `ch4 po2` = col_number(),
...8 = col_skip()), trim_ws = TRUE) ## New names:
## • `` -> `...8`Background rates
Pre-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",preexperiment_date_asus,"slopes"))
pre_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
pre_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_pre1 <- pre_cycle1 %>% calc_rate.bg()##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.00233724post-experiment
setwd(paste("C:/Users/jc527762/OneDrive - James Cook University/PhD dissertation/Data/2023/Resp_backup/2023_Resp/Asus/Experiment_",postexperiment_date_asus,"slopes"))
post_cycle1 <- read_delim("./Cycle_1.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle2 <- read_delim("./Cycle_2.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
post_cycle3 <- read_delim("./Cycle_3.txt",
delim = ";", escape_double = FALSE, col_types = cols(Time = col_time(format = "%H:%M:%S"),
...8 = col_skip()), trim_ws = TRUE) %>%
rename(dTIME = `Seconds from start for linreg`,
ch1 =`ch1 po2`,
ch2 =`ch2 po2`,
ch3 =`ch3 po2`,
ch4 =`ch4 po2`) %>%
select(c("Time",chamber))
bg_post1 <- post_cycle1 %>% calc_rate.bg() ##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------##
## # plot.calc_rate.bg # -------------------
## plot.calc_rate.bg: Plotting all 1 background rates ...
## -----------------------------------------## [1] -0.00385921Resting metabolic rate
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
#### subset data
Tstart.row=which(firesting2_asus$TIME ==Cycle_1_asus$Time[1], firesting_asus$TIME)
Tstart.dTIME=as.numeric(firesting2_asus[Tstart.row, "dTIME"])
Tend.row=which(firesting2_asus$TIME ==tail(Cycle_last_asus$Time, n=1), firesting_asus$TIME)
Tend.dTIME=as.numeric(firesting2_asus[Tend.row, "dTIME"])
apoly_insp <- firesting2_asus |>
subset_data(from=Tstart.dTIME,
to=Tend.dTIME,
by="time")
apoly_insp <- inspect(apoly_insp, time=1, oxygen=2)##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.45
## -----------------------------------------
Extract rates
apoly_cr.int <- calc_rate.int(apoly_insp,
starts=(195+45+300),
wait=45,
measure=245,
by="time",
plot=TRUE) ##
## # plot.calc_rate.int # ------------------
## plot.calc_rate.int: Plotting rate from all replicates ...## plot.calc_rate.int: Plotting first 20 selected reps only. To plot others modify 'pos' input.
## -----------------------------------------adjust rates for background
apoly_cr.int_adj <- adjust_rate(apoly_cr.int,
by = bg_pre,
by2 = bg_post,
time_by = Tstart.row,
time_by2 = Tend.row,
method = "linear")## Warning: adjust_rate: One or more of the timestamps for the rate(s) in 'x' do not lie between the timestamps for the 'by' and 'by2' background rates.
## Ensure this is correct. The adjustment value has been calculated regardless by extrapolating outside the background rates time window.## adjust_rate: Rate adjustments applied using "linear" method.Converting units
apoly_cr.int_adj2 <- apoly_cr.int_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253) ## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.Plot curve
ggplot(as.data.frame(apoly_cr.int_adj2$summary), aes(x=row, y=rate.output*-1)) +
geom_point() +
stat_smooth(method = "lm", formula = y~poly(x, 2), color="red") +
theme_classic()
Rate filtering
apoly_rmr <- apoly_cr.int_adj2 |>
select_rate(method ="rsq", n=c(0.95,1)) |>
select_rate(method="lowest", n=6) |>
plot(type="full") |>
summary(export = TRUE)## select_rate: Selecting rates with rsq values between 0.95 and 1...## ----- Selection complete. 0 rate(s) removed, 21 rate(s) remaining -----## select_rate: Selecting lowest 6 *absolute* rate values...## ----- Selection complete. 15 rate(s) removed, 6 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: 10 1 465.2775 -0.02340100 0.996 NA 3610 3790 15776.28
## 2: 11 1 479.3481 -0.02346401 0.993 NA 4007 4187 16316.17
## 3: 12 1 495.0493 -0.02364803 0.994 NA 4405 4585 16856.21
## 4: 13 1 490.5413 -0.02264970 0.992 NA 4803 4983 17397.32
## 5: 16 1 585.5557 -0.02569501 0.997 NA 5994 6175 19016.67
## 6: 19 1 632.2438 -0.02593264 0.997 NA 7188 7368 20637.50
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 16020.92 95.948 90.209 -0.02340100 -0.003814789 -0.01958621 -0.01958621
## 2: 16560.76 96.174 90.635 -0.02346401 -0.003915383 -0.01954863 -0.01954863
## 3: 17101.05 96.097 90.490 -0.02364803 -0.004016034 -0.01963200 -0.01963200
## 4: 17642.21 96.099 90.738 -0.02264970 -0.004116866 -0.01853283 -0.01853283
## 5: 19261.81 96.609 90.405 -0.02569501 -0.004418628 -0.02127638 -0.02127638
## 6: 20882.92 96.844 90.611 -0.02593264 -0.004720669 -0.02121197 -0.02121197
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0005429 NA 36 30 1.013253 -0.1990811
## 2: %Air sec 0.04565 0.0005429 NA 36 30 1.013253 -0.1986991
## 3: %Air sec 0.04565 0.0005429 NA 36 30 1.013253 -0.1995465
## 4: %Air sec 0.04565 0.0005429 NA 36 30 1.013253 -0.1883742
## 5: %Air sec 0.04565 0.0005429 NA 36 30 1.013253 -0.2162606
## 6: %Air sec 0.04565 0.0005429 NA 36 30 1.013253 -0.2156059
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -366.6993 NA mgO2/hr/kg -366.6993
## 2: -365.9957 NA mgO2/hr/kg -365.9957
## 3: -367.5567 NA mgO2/hr/kg -367.5567
## 4: -346.9777 NA mgO2/hr/kg -346.9777
## 5: -398.3434 NA mgO2/hr/kg -398.3434
## 6: -397.1374 NA mgO2/hr/kg -397.1374
## -----------------------------------------Results
results <- data.frame(Clutch = Clutch,
Replicate =Replicate,
Male=Male,
Female=Female,
Population = Population,
Tank = Tank,
Mass = mass,
Chamber = chamber,
System = system1,
Volume = chamber_vol,
Date_tested = Date_tested,
Date_analysed =Date_analysed,
Swim = Swim,
Salinity = salinity,
Temperature = as.numeric(unique(firesting2$temperature)),
Resting_kg = mean(apoly_rmr$rate.output*-1),
Resting = mean(apoly_rmr$rate.output*-1)*mass,
rsqrest =mean(apoly_rmr$rsq))
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 8 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0005429 | ch1 | Asus | 0.04565 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 379.1465 | 0.2058386 | 0.9954 |
Maximum oxygen consumption
Data manipulation
Inspect file
## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.96
## -----------------------------------------
Subset data
cycle1.start <- Cycle_1.mmr[1,1]
cycle1.end <- tail(Cycle_1.mmr, n=1)[1,1]
cycle1.start.row <- which(firesting2_mmr$TIME == cycle1.start); cycle1.start## Warning in which(firesting2_mmr$TIME == cycle1.start): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="## Warning in which(firesting2_mmr$TIME == cycle1.end): Incompatible methods
## ("Ops.difftime", "Ops.data.frame") for "=="cycle1_data <- firesting2_mmr |>
subset_data(from = cycle1.start.row,
to = cycle1.end.row,
by = "row") ## subset_data: Multi-column dataset detected in input!
## subset_data is generally intended to subset data already passed through inspect(), or 2-column data frames where time and oxygen are in columns 1 and 2 respectively.
## Subsetting will proceed anyway based on this assumption, but please ensure you understand what you are doing.## inspect: Applying column default of 'time = 1'## inspect: Applying column default of 'oxygen = 2'## Warning: inspect: Time values are not evenly-spaced (numerically).## inspect: Data issues detected. For more information use print().##
## # print.inspect # -----------------------
## dTIME ch1
## numeric pass pass
## Inf/-Inf pass pass
## NA/NaN pass pass
## sequential pass -
## duplicated pass -
## evenly-spaced WARN -
##
## Uneven Time data locations (first 20 shown) in column: dTIME
## [1] 1 2 3 4 5 6 7 8 9 10 11 12 14 15 16 17 18 21 22 23
## Minimum and Maximum intervals in uneven Time data:
## [1] 1.33 2.45
## -----------------------------------------
Calculating MMR
## Warning: auto_rate: Multi-column dataset detected in input. Selecting first two columns by default.
## If these are not the intended data, inspect() or subset the data frame columns appropriately before running auto_rate()
##
## # summary.auto_rate # -------------------
##
## === Summary of Results by Highest Rate ===
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 626.0288 -0.04857926 0.9945260 NA 24 69 10904.29
## 2: NA 2 625.9456 -0.04857187 0.9945344 NA 25 70 10905.67
## 3: NA 3 625.7243 -0.04855138 0.9945260 NA 23 68 10902.89
## 4: NA 4 625.4703 -0.04852809 0.9945331 NA 22 67 10901.56
## 5: NA 5 626.3150 -0.04847074 0.9976016 NA 151 196 11079.29
## ---
## 170: NA 170 361.2119 -0.02446285 0.9650019 NA 77 122 10976.10
## 171: NA 171 360.4263 -0.02439200 0.9657533 NA 78 123 10977.44
## 172: NA 172 360.3973 -0.02439094 0.9666757 NA 81 126 10981.53
## 173: NA 173 358.4536 -0.02421394 0.9683563 NA 80 125 10980.18
## 174: NA 174 358.2389 -0.02419395 0.9685499 NA 79 124 10978.80
## endtime oxy endoxy rate
## 1: 10964.29 96.361 93.379 -0.04857926
## 2: 10965.67 96.346 93.274 -0.04857187
## 3: 10962.89 96.437 93.441 -0.04855138
## 4: 10961.56 96.487 93.501 -0.04852809
## 5: 11139.29 89.403 86.401 -0.04847074
## ---
## 170: 11036.10 92.909 91.128 -0.02446285
## 171: 11037.44 92.838 91.089 -0.02439200
## 172: 11041.53 92.647 90.932 -0.02439094
## 173: 11040.18 92.725 91.002 -0.02421394
## 174: 11038.80 92.854 91.052 -0.02419395
##
## Regressions : 174 | Results : 174 | Method : highest | Roll width : 60 | Roll type : time
## -----------------------------------------Adjusting
## adjust_rate: Rate adjustments applied using "mean" method.##
## # print.adjust_rate # -------------------
## NOTE: Consider the sign of the adjustment value when adjusting the rate.
##
## Adjustment was applied using the 'mean' method.
##
## Rank 1 of 174 adjusted rate(s):
## Rate : -0.04857926
## Adjustment : -0.00233724
## Adjusted Rate : -0.04624202
##
## To see other results use 'pos' input.
## To see full results use summary().
## -----------------------------------------Converting units
mmr_adj2 <- mmr_adj |>
convert_rate(oxy.unit = "%Air",
time.unit = "secs",
output.unit = "mg/h/kg",
volume = chamber_vol,
mass = mass,
S = salinity,
t = as.numeric(unique(firesting2$temperature)),
P = 1.013253)## convert_rate: Object of class 'adjust_rate' detected. Converting all adjusted rates in '$rate.adjusted'.selecting rates
mmr_final <- mmr_adj2 |>
select_rate(method = "rsq", n=c(0.93,1)) |>
select_rate(method = "highest", n=1) |>
plot(type="full") |>
summary(export=TRUE)## select_rate: Selecting rates with rsq values between 0.93 and 1...## ----- Selection complete. 0 rate(s) removed, 174 rate(s) remaining -----## select_rate: Selecting highest 1 *absolute* rate values...## ----- Selection complete. 173 rate(s) removed, 1 rate(s) remaining -----##
## # plot.convert_rate # -------------------## plot.convert_rate: Plotting all rate(s)...
## -----------------------------------------
##
## # summary.convert_rate # ----------------
## Summary of all converted rates:
##
## rep rank intercept_b0 slope_b1 rsq density row endrow time
## 1: NA 1 626.0288 -0.04857926 0.994526 NA 24 69 10904.29
## endtime oxy endoxy rate adjustment rate.adjusted rate.input
## 1: 10964.29 96.361 93.379 -0.04857926 -0.00233724 -0.04624202 -0.04624202
## oxy.unit time.unit volume mass area S t P rate.abs
## 1: %Air sec 0.04565 0.0005429 NA 36 30 1.013253 -0.4700201
## rate.m.spec rate.a.spec output.unit rate.output
## 1: -865.7582 NA mgO2/hr/kg -865.7582
## -----------------------------------------Results
results <- results |>
mutate(Max_kg = mmr_final$rate.output*-1,
Max = (mmr_final$rate.output*-1)*mass,
rsqmax =mmr_final$rsq,
AAS_kg = Max_kg - Resting_kg,
AAS = Max - Resting,
Notes=Notes,
True_resting="")
knitr::kable(results, "simple") | Clutch | Replicate | Male | Female | Population | Tank | Mass | Chamber | System | Volume | Date_tested | Date_analysed | Swim | Salinity | Temperature | Resting_kg | Resting | rsqrest | Max_kg | Max | rsqmax | AAS_kg | AAS | Notes | True_resting |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 58 | 8 | CVLA089 | CVLA059 | Vlassof cay | 370 | 0.0005429 | ch1 | Asus | 0.04565 | 2023-03-29 | 2024-06-27 | good/good | 36 | 30 | 379.1465 | 0.2058386 | 0.9954 | 865.7582 | 0.4700201 | 0.994526 | 486.6117 | 0.2641815 | ||
| ### Expor | ting data |
## Rows: 285 Columns: 25
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (9): Male, Female, Population, Chamber, System, Date_tested, Swim, Note...
## dbl (16): Clutch, Replicate, Tank, Mass, Volume, Date_analysed, Salinity, Te...
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.